Differential gene expression analysis for multi-subject single-cell RNA-sequencing studies with aggregateBioVar.

ABSTRACT:

Motivation

Single-cell RNA-sequencing (scRNA-seq) provides more granular biological information than bulk RNA-sequencing; bulk RNA sequencing remains popular due to lower costs which allows processing more biological replicates and design more powerful studies. As scRNA-seq costs have decreased, collecting data from more than one biological replicate has become more feasible, but careful modeling of different layers of biological variation remains challenging for many users. Here, we propose a statistical model for scRNA-seq gene counts, describe a simple method for estimating model parameters and show that failing to account for additional biological variation in scRNA-seq studies can inflate false discovery rates (FDRs) of statistical tests.

Results

First, in a simulation

SUBMITTER: Thurman AL 

PROVIDER: S-EPMC8504643 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature